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US Patent 11995546 Ensemble neural network state machine for detecting distractions

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Contents

Is a
Patent
Patent
0

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
0
Patent Number
119955460
Patent Inventor Names
John Bicket0
Brian Tuan0
Abner Ayala Acevedo0
Sharan Srinivasan0
Bruce Kellerman0
Muhammad Ali Akhtar0
Vincent Shieh0
Jing Wang0
Date of Patent
May 28, 2024
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Patent Application Number
178115120
Date Filed
July 8, 2022
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Patent Citations
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US Patent 10390227 Authentication of a gateway device in a sensor network
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US Patent 11386325 Ensemble neural network state machine for detecting distractions
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US Patent 11451610 Remote asset monitoring and control
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US Patent 11451611 Remote asset notification
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US Patent 11479142 Estimated state of charge determination
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US Patent 11522857 Video gateway for camera discovery and authentication
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US Patent 11558449 Industrial controller system and interactive graphical user interfaces related thereto
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US Patent 11599097 Modular industrial controller system
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...
Patent Citations Received
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US Patent 12106613 Dynamic delivery of vehicle event data
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US Patent 12128919 Dash cam with artificial intelligence safety event detection
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US Patent 12126917 Dual-stream video management
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US Patent 12117546 Systems and methods of remote object tracking
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Patent Primary Examiner
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Peter D Nolan
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CPC Code
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G06N 20/20
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B60W 2540/225
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B60W 2540/229
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B60W 2540/223
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B60W 2420/42
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B60W 2420/54
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G06N 3/08
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B60W 50/14
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Patent abstract

A vehicle device may execute one or more neural networks (and/or other artificial intelligence), based on input from one or more of the cameras and/or other sensors, to intelligently detect safety events in real-time. The one or more neural networks may be an ensemble neural network that includes neural networks for detecting a head and hand of a user, neural networks for detecting hand actions of the user, neural networks for detecting the head pose of the user, neural networks for predicting an occurrence of an event, and neural networks for predicting a start time and end time of the event. Further, the neural networks can be segmented into a modular neural network based on metadata. The segmentation of the neural network can define a thin layer of the modular neural network to enable independent tuning of the thin layer of the modular neural network.

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